Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification
Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification
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DOI：
10.1109/mnet.011.2000492
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发表时间：
2021-05-01
期刊：
影响因子：
9.3
通讯作者：
Mejri, Siefeddine
中科院分区：
文献类型：
--
作者：
Hamdaoui, Bechir;Elmaghbub, Abdurrahman;Mejri, Siefeddine
基于射频（RF）数据的基于深度学习的无线设备分类的大多数先前工作都使用现成的深度神经网络（DNN）模型，这些模型主要用于视觉和语言等领域。然而，无线射频数据具有与其他领域不同的独特特征。例如，射频数据包含由底层硬件和协议配置决定的混合时间和频率特征。此外，由于这些信号本身包含重复模式（PHY导频、帧前缀等），无线RF通信信号表现出循环平稳性。在本文中，我们首先解释并展示了目前提出的用于无线设备分类的现有DNN特征设计方法的不适用性和局限性。然后，我们提出了新的特征设计方法，利用射频通信信号的独特结构和发射机硬件损伤引起的频谱发射，定制适合使用射频信号数据对无线设备进行分类的DNN模型。我们提出的深度神经网络特征设计在可扩展性、准确性、签名抗克隆和对环境扰动的不敏感性方面大大提高了分类稳健性。在文章的最后，我们提出了其他具有很大潜力的特征设计策略，这些策略可以进一步提高基于dnn的无线设备分类的性能，并讨论了与这些提议策略相关的开放研究挑战。
Most prior works on deep learning-based wireless device classification using radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models, which were matured mainly for domains like vision and language. However, wireless RF data possesses unique characteristics that differentiate it from these other domains. For instance, RF data encompasses intermingled time and frequency features that are dictated by the underlying hardware and protocol configurations. In addition, wireless RF communication signals exhibit cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, and so on) that these signals inherently contain. In this article, we begin by explaining and showing the unsuitability as well as limitations of existing DNN feature design approaches currently proposed to be used for wireless device classification. We then present novel feature design approaches that exploit the distinct structures of RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations. We end the article by presenting other feature design strategies that have great potential for providing further performance improvements of the DNN-based wireless device classification, and discuss the open research challenges related to these proposed strategies.